The focus of this effort is the efficient grouping of events in a flow cytometry list mode data file into clusters that represent distinct cell populations. Cluster analysis alone is efficient when it is applied to only a few thousand events in a list mode file. After these first few thousand events have been clustered, the means and standard deviations of the clusters are known. A fast technique such as Classification and Regression Trees (CART) can then be applied to the data remaining in the file to complete the partitioning of the dataset. The multidimensional space is effectively divided into partitions. A FORTRAN program has been developed to carry out the CART calculations on flow cytometry data. It is now being translated into C++ and incorporated into the Macintosh clustering program described in another subproject. This technique will be particularly useful for studies of residual disease. If cluster analysis and CART procedures are done on data from a leukemia patient before treatment, the CART boundaries can be used to determine the number of cells remaining in the leukemia population as the treatment proceeds.